This Article Full Text (PDF) All Versions of this Article: 0962280208099500v1 18/5/453    most recent References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Scopus Google Scholar Articles by Miklós, I. Articles by Hein, J. PubMed PubMed Citation Articles by Miklós, I. Articles by Hein, J. Social Bookmarking                         What's this?

Stochastic models of sequence evolution including insertion—deletion events

Bioinformatics Group, Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, 1053 Budapest, Reáltanoda u. 13-15, Hungary, miklosi{at}renyi.hu, Bioinformatics Group, Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK, Data Mining and Search Research Group, Computer and Automation Institute, Hungarian Academy of Sciences, 1111 Budapest, Lágymányosi u. 11., Hungary

Ádám Novák

Bioinformatics Group, Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK

Rahul Satija

Bioinformatics Group, Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK

Rune Lyngsø

Bioinformatics Group, Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK

Jotun Hein

Bioinformatics Group, Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK

Comparison of sequences that have descended from a common ancestor based on an explicit stochastic model of substitutions, insertions and deletions has risen to prominence in the last decade. Making statements about the positions of insertions-deletions (abbr. indels) is central in sequence and genome analysis and is called alignment. This statistical approach is harder conceptually and computationally, than competing approaches based on choosing an alignment according to some optimality criteria. But it has major practical advantages in terms of testing evolutionary hypotheses and parameter estimation. Basic dynamic approaches can allow the analysis of up to 4—5 sequences. MCMC techniques can bring this to about 10—15 sequences. Beyond this, different or heuristic approaches must be used. Besides the computational challenges, increasing realism in the underlying models is presently being addressed. A recent development that has been especially fruitful is combining statistical alignment with the problem of sequence annotation, making statements about the function of each nucleotide/amino acid. So far gene finding, protein secondary structure prediction and regulatory signal detection has been tackled within this framework. Much progress can be reported, but clearly major challenges remain if this approach is to be central in the analyses of large incoming sequence data sets.

This version was published on October 1, 2009

Statistical Methods in Medical Research, Vol. 18, No. 5, 453-485 (2009)
DOI: 10.1177/0962280208099500


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?